st: RE: One v. two-step ECMs

The equivalence between the one-step and two-step methods is an asymptotic result. Banerjee, Dolado, Galbraith and Hendry in their 1993 book (I don't have the title at hand) on cointegration have shown that for even large finite samples the bias in the static equation estimated in the first step can be quite large.
You can also Google for a paper by Hendry and Juselius, Explaining Cointegration, Part 1
Eric de Souza
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-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Neesha Harnam
Sent: 02 February 2012 11:18
To: statalist@hsphsun2.harvard.edu
Subject: st: One v. two-step ECMs
Dear statalist users,
I have an error-correction model for analyzing panel data (n=70, t=30) and am having difficulty figuring out if and where I went wrong with my coding. The results for the one- and two-step ECMs are very different (especially in terms of coefficients and significance) depending on the model used, and my understanding was that they estimated the same thing. The differences also appear in the indicator variables once I add them to my model. GDP per capita is I(1) but stationary in differences, while H1, a health outcome, is I(0). IND1-4 are dummy variables that show whether a country experienced a particular event in a given year.
Two-step process:
regress H1 log_gdppc, vce(cluster countrycode) predict e, resid regress DH1 Dlog_gdppc L.e, vce(cluster countrycode)
Two-step with indicators:
regress DH1 Dlog_gdppc L.e IND1 IND2 IND3 IND4, vce(cluster countrycode)
One-step process:
regress DH1 Dlog_gdppc L.H1 L.log_gdppc, vce(cluster countrycode)
One-step with indicators:
regress DH1 Dlog_gdppc L.H1 L.log_gdppc IND1 IND2 IND3 IND4, vce(cluster countrycode)
Any help would be greatly appreciated.
Thank you,
Neesha
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